Compute the mean and standard deviation of the provided sample data on cost of living adjustments. Then compute the z-scores for the smallest and largest comparative salaries and housing adjustments and interpret your results. Question content area bottomPart 1Calculate the mean and standard deviation for the comparative salary and housing columns.(Round to two decimal places as needed.) Comparative SalaryHousingMean$enter your response here enter your response here %Standard Deviation$enter your response here enter your response here % City Comparative Salary HousingLos Angeles 83795 168Charlotte 58012 5New York 136024 479St. Louis 56084 -9Minneapolis 65060 43Austin 57530 13New Orleans 57530 24Columbus 54578 -1Seattle 83253 133Chicago 70000 73San Francisco 105241 304Philadelphia 72048 72Charleston 60904 22Boston 85904 141Detroit 57590 15

Answered: Compute the mean and standard deviation…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question content area bottom

Part 1

Calculate the mean and standard deviation for the comparative salary and housing columns.

(Round to two decimal places as needed.)

	Comparative Salary	Housing
Mean	$enter your response here	enter your response here %
Standard Deviation	$enter your response here	enter your response here %

City Comparative Salary Housing
Los Angeles 83795 168
Charlotte 58012 5
New York 136024 479
St. Louis 56084 -9
Minneapolis 65060 43
Austin 57530 13
New Orleans 57530 24
Columbus 54578 -1
Seattle 83253 133
Chicago 70000 73
San Francisco 105241 304
Philadelphia 72048 72
Charleston 60904 22
Boston 85904 141
Detroit 57590 15

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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